Abstract
Automatic intensity-based tissue classification sets requirements for the quality of multispectral magnetic resonance (MR) images. Tests for evaluating the separability of tissue classes, and on the other hand class distances required to obtain reliable classification, are presented in this study. Intraslice, interslice and interpatient training schemes for 5-nn classification were considered. Interslice training was utilized in classification of images from 10 patients with ischemic stroke giving results of satisfactory but highly variable quality. Based on the experience with these data sets, similar tests are recommended before imaging a large patient series in order to avoid extra manual work and to obtain reliable classification results.
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